arXiv Machine Learning July 28, 2026 By Siyuan Du, Mengxi Chen, Xinyang Jiang, Zilong Wang, Jiangchao Yao, Dongsheng Li, Ya Zhang, Lili Qiu, Yanfeng Wang

Dementia Etiology Diagnosis via Collaborative Meta Knowledge Enhancement

Read the original on arXiv Machine Learning →

arXiv:2607. 22770v1 Announce Type: new Abstract: Although artificial intelligence (AI) has shown promising performance in several medical tasks, accurate dementia etiology diagnosis with AI remains challenging due to complex overlapping symptoms among diseases.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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Bayesian meta-learning for modeling Alzheimer's disease progression

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Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts

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PatTree: a novel approach for automated creation of multimodal, graph-based patient representations for medical classification tasks

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By Julia Gehrmann, Lars Quakulinski, Hamza Naseem, Oya Beyan

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arXiv AI

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Technological Advances in Detecting and Managing Cognitive Impairment in Older Adults: Trends, Challenges, and Future Directions

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By Mohammad Asif, Azizuddin Khan, Mohd Azam, Anurag Rajkumar Bombarde

llms multimodal safety

arXiv AI

Jun 24

LLM-MINE: Large Language Model based Alzheimer's Disease and Related Dementias Phenotypes Mining from Clinical Notes

arXiv:2603. 13673v2 Announce Type: replace Abstract: Accurate extraction of Alzheimer's Disease and Related Dementias (ADRD) phenotypes from electronic health records (EHR) is critical for early-stage detection and disease staging.

By Mingchen Shao, Yuzhang Xie, Carl Yang, Jiaying Lu

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Supervised Fine-tuning with Synthetic Rationale Data Hurts Real-World Disease Prediction

Supervised fine-tuning with synthetic rationale data is widely assumed to improve language model performance on clinical prediction tasks by teaching models not just what to predict but why. We test this assumption on five-year Alzheimer's disease and related dementias (ADRD) prediction from longitudinal health histories.

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Bayesian meta-learning for modeling Alzheimer's disease progression

Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts

PatTree: a novel approach for automated creation of multimodal, graph-based patient representations for medical classification tasks

Technological Advances in Detecting and Managing Cognitive Impairment in Older Adults: Trends, Challenges, and Future Directions

LLM-MINE: Large Language Model based Alzheimer's Disease and Related Dementias Phenotypes Mining from Clinical Notes

Supervised Fine-tuning with Synthetic Rationale Data Hurts Real-World Disease Prediction